1. Field of the Invention
This invention relates generally to a system and method for determining vehicle dynamics and, more particularly, to a system and method for determining vehicle speed and position that employs radar, lidar and/or camera signals.
2. Discussion of the Related Art
Various driver assist systems and autonomous driving operations in vehicles, such as electronic stability control (ECS), adaptive cruise control (ACC), lane keeping (LK), lane changing (LC), etc., require the development of highly robust and precise modules for estimating various vehicle dynamics. Such modules are necessary to provide knowledge of the vehicle position and velocity to control the vehicle along a desired state.
Currently, micro-electromechanical system (MEMS) based inertial measurement units (IMUs) and wheel speed sensors are used to provide vehicle speed. However, the performance of wheel speed sensors is reduced during wheel slippage conditions, such as when the driver performs cornering and swerving maneuvers. Therefore, a dead-reckoning strategy for an IMU is utilized at these times to produce vehicle velocity and position of the vehicle. Because MEMS IMUs usually have larger errors than expensive gyro-systems, errors in position and velocity can grow rapidly. Thus, current automotive-grade MEMS IMUs alone are typically not suitable for dead-reckoning for a long period of time.
It has been proposed in the art to integrate GPS and a low cost MEMS IMU to address the non-zero bias and drift issues of an IMU. However, few of these systems address the issue that the GPS signals are not always available, such as when the vehicle is in “urban canyons” where an insufficient number of satellites are tracked to determine the position and the velocity of the vehicle.
Future advanced driver assist systems (ADS) for vehicles will include various object detection sensors, such as long-range radar and lidar sensors and ultrasonic parking aid sensors. Further, camera-based systems for lane departure warning are currently being developed. Thus, there has been an increased interest in utilizing data from these devices to estimate vehicle self-motion. For example, one system proposes to use an in-vehicle mounted calibrating camera to estimate self-motion and to detect moving objects on roads. Another proposed system uses a single camera for computing ego-motion of the vehicle based on optical flow. Further work includes the use of stereo-vision to the ego-pose estimation problem in urban environments. However, none of these approaches alone is reliable enough in cluttered scenes. Further, few of these systems make explicit the essential need for system integration that will be necessary in the future commercial development of this technology.